BLOG Archives - Page 4 of 18 - Cambridge Semantics

SPARQL – One Standard to Rule Them All

I thought I need SHACL, SWRL, ShEx, SPIN, R2RML and more to handle a Semantic RDF Graph – I was proved wrong.

Why a Data Fabric wins out over traditional architecture

See why data fabric architecture excels where traditional enterprise data systems and shared data situations fail.

The differences between data integration architecture concepts

Understand the differences between different data integration architectures like data fabrics, data mesh, and FAIR data.

Merck KGaA, Bosch, and Deloitte share their knowledge graph stories

Leaders from Merck KGaA, Bosch, and Deloitte discuss their vision for knowledge graph technology and how they are translating that vision into reality within their own Digital Twin, Regulatory Data Fabric, and Operational Resilience initiatives.

Build knowledge cards of your data, the way Google does for search

To accomplish this data surfacing from a simple query, you need a data fabric. The data fabric being the metadata layer that ties your simple search query to all the common results a user would want surfaced, forming your very own knowledge card.

Why There’s A Need for Knowledge Graphs

This blog post explores how a knowledge graph has the ability to support agile development and data integration while connecting data across the enterprise.

5 Common Barriers to Launching a Knowledge Graph Project

Across industries our technical experts have found themes in obstacles to launching a knowledge graph project. Here are the common barriers and how to avoid them.

Managed Vocabularies, Ontologies, and an Enterprise Scale Knowledge Graph: A Primer

This blog provides a primer on the concepts of managed vocabularies and ontologies and how they relate to and can be implemented in a knowledge graph. This is an excerpt from the ebook The Rise of the Knowledge Graph.

Knowledge Graph: Who Cares? And What Can One Do With It?

In a previous post that described knowledge graphs and their benefits, we discussed how knowledge graphs help organizations make more of their enterprise data easy to find, understand, and use. This post argues for knowledge graph technology and discusses common application areas of knowledge graphs.

Machine Understandable Context and Why It Matters

This post continues the larger discussion of knowledge graphs, their roles, uses and significance. We discuss context, it’s importance, and how to establish it using formal ontology to create machine understandable information. It is useful to read posts, such as Knowledge Graphs: A Love Affair and The Rise of The Knowledge Graph, to understand this article…in context!

Knowledge Graphs: Origins, Inhibitors and Breakthroughs

This post discusses some history of knowledge graph technology, what went wrong, and key advances that have allowed knowledge graphs to reenter the enterprise “data arena.” We also highlight some important outcomes of the technology.

AI, ML and Advanced Analytics Strategies: Look Before You Leap

The phrase, “garbage in, garbage out” comes to mind. In other words, logically, if one’s premise is flawed, so also are one’s conclusions. Nevertheless, we can’t seem to tame our fascination with “shiny objects.” This article discusses a “prerequisite” foundation on which to build successful AI, ML and advanced analytics capabilities.